Breast Cancer Polygenic Risk Score Associated with Outcomes after <i>In Situ</i> Breast Disease
Bibliographic record
Abstract
BACKGROUND: Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are preinvasive breast lesions. DCIS is treated more aggressively, as it is more likely to develop into invasive disease than LCIS. Both DCIS and LCIS face an elevated risk of contralateral breast cancer. It is thus important to identify those at high risk of further breast disease in order to personalize treatment. METHODS: This study evaluated whether the 313-SNP breast cancer polygenic risk score (PRS313) can predict the likelihood of developing ipsilateral or contralateral breast cancer after diagnosis of DCIS or LCIS by analyzing data from patients diagnosed with DCIS (N = 2,169) or LCIS (N = 185) from the Investigate the genetiCs of In situ Carcinoma of the ductaL subtypE (ICICLE) and A study to investigate the Genetics of LobulAr Carcinoma In situ in EuRope (GLACIER) studies, with a median follow-up of 11 years. Outcomes included any further in situ or invasive breast disease (including distant metastasis), ipsilateral breast disease, invasive ipsilateral breast disease, and contralateral breast disease. RESULTS: Cox regression analysis revealed a significant association between increasing continuous PRS313 and the risk of contralateral disease following DCIS (HR = 1.30; 95% confidence interval, 1.08-1.56) and a link between PRS313 and ipsilateral disease after LCIS (HR = 2.16; 95% confidence interval, 1.22-3.81). CONCLUSIONS: This research provides strong evidence that PRS313 can serve as a valuable predictor of future breast cancer events in women with in situ breast cancer, specifically contralateral disease after DCIS and ipsilateral disease after LCIS. IMPACT: PRS313 has the potential to guide clinical decisions about surveillance, risk-reduction treatments, and personalized care in those with in situ breast cancers, which could improve outcomes and optimize the use of healthcare resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".